Automated generation of modeling language profiles

ABSTRACT

A database of data can be leveraged to automatically generate profiles for use in modeling. The data may be specific to one or more domains and/or general to multiple domains. A mapping scheme is established to map structure of the database to a structure of a profile. The mapping scheme is read and the database is read in accordance with the mapping scheme to automatically generate one or more profiles.

TECHNICAL FIELD

Embodiments of the inventive subject matter generally relate to thefield of modeling, and, more particularly, to automated generation ofmodeling language profiles.

BACKGROUND

The Unified Modeling Language™ (UML™) is the industry-standard languagefor specifying, visualizing, constructing, and documenting the artifactsof software systems. UML can be used to model deployment of anapplication, upgrade, configuration, etc., in an environment.

One of the standard UML diagrams is a deployment diagram, whichgraphically depicts a deployment model. The deployment diagram showsdeployment to components of the environment (e.g., servers, clients,connections, virtual machines, etc.). A deployment diagram can visuallydepict where the different components of the system will physically runand how they will communicate with each other.

SUMMARY

A method comprises reading a mapping scheme that maps structure of adatabase to structure of a profile. The profile is for use in creating amodel in accordance with a modeling language. Metadata is read out froma database in accordance with the mapping scheme. An instance of theprofile is created with the metadata read out of the database inaccordance with the mapping scheme.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments may be better understood, and numerous objects,features, and advantages made apparent to those skilled in the art byreferencing the accompanying drawings.

FIG. 1 conceptually depicts an example tool that automates generation ofexecutable code from a deployment model.

FIGS. 2-5 depict a flowchart of example operations to automaticallygenerate a deployment activity model from a semantically correctdeployment topology model.

FIG. 2 depicts a first portion of the flowchart of example operations toautomatically generate a deployment activity model from a semanticallycorrect deployment topology model.

FIG. 3 depicts a second portion of the flowchart of example operationsto automatically generate a deployment activity model from asemantically correct deployment topology model.

FIG. 4 depicts a third portion of the flowchart of example operations toautomatically generate a deployment activity model from a semanticallycorrect deployment topology model.

FIG. 5 depicts a fourth portion of the flowchart of example operationsto automatically generate a deployment activity model from asemantically correct deployment topology model.

FIGS. 6-8 depict flowcharts of examples operations for generatingexecutable code from a semantically correct deployment activity model.FIGS. 6-7 depict a flowchart of example operations for generating alinear representation of a semantically correct deployment activitymodel.

FIG. 6 depicts a first portion of the flowchart of example operationsfor generating a linear representation of a semantically correctdeployment activity model.

FIG. 7 depicts a second portion of the flowchart of example operationsfor generating an intermediate representation of a semantically correctdeployment activity model.

FIG. 8 depicts a flowchart of example operations for generatingexecutable code to orchestrate performance of the activities of thedeployment activity model.

FIG. 9 depicts a conceptual example of transforming metadata in adatabase into a profile.

FIG. 10 depicts an example conceptual diagram of stereotype aggregationacross profiles.

FIG. 11 depicts a flowchart of example operations for aggregatingconstraints across profiles to define a stereotype.

FIG. 12 depicts an example computer system.

DESCRIPTION OF EMBODIMENT(S)

The description that follows includes exemplary systems, methods,techniques, instruction sequences and computer program products thatembody techniques of the present inventive subject matter. However, itis understood that the described embodiments may be practiced withoutthese specific details. In other instances, well-known instructioninstances, protocols, structures and techniques have not been shown indetail in order not to obfuscate the description.

Although a modeling language can be used to create a deployment diagramto graphically depict a deployment model, the utility of the deploymentdiagram is limited to easing understanding of the model. Automatedgeneration of executable deployment code that implements a deploymentmodel allows benefits of modeling to be realized. Automated generationof executable deployment code from a deployment model providesefficiency in time and resource utilization.

FIG. 1 conceptually depicts an example tool that automates generation ofexecutable code from a deployment model. A computer 107 hosts adeployment tool 109. The deployment tool 109 allows a deploymenttopology model 101 to be created. The deployment tool 109 ensures thatthe deployment topology model 101 is semantically correct. For example,as a user creates the deployment topology model 101 the deployment modeltool 109 generates warnings that attributes have not been defined, thatrelationships violate constraints, that values are not allowed, etc.

The deployment tool 109 automatically generates a deployment activitymodel 103 from the deployment topology model 101. With the deploymentmodel tool 109, a user can define values and/or modify the deploymentactivity model 103. The deployment model tool 109 can also automaticallydefine attributes (e.g., use default values, read values from a file,etc.). As with the deployment topology model 101, the deployment modeltool 109 ensures that the deployment activity model 103 is semanticallycorrect (“validates the model”).

After validating the deployment activity model 103, the deployment modeltool 109 generates executable code 105 that orchestrates performance ofactivities indicated the deployment activity model 103. The deploymentmodel tool 109 walks the deployment activity model 103 and looks upexecutable code units associated with the activities indicated in thedeployment activity model 103. The deployment model tool 109 createscalls to these code units for the activities traversed in the deploymentactivity model 103 to generate the executable activity orchestrationcode 105. With the automatically generated executable orchestration code105, deployment can be carried out over an enterprise system. Theexecutable orchestration code 105 can deploy an application,configuration, add-on, update, etc.

The generation of executable orchestration code to carry out adeployment over a system was depicted in FIG. 1 as involving generationof a deployment activity model from a semantically correct deploymenttopology model and generation of the executable orchestration code fromthe deployment activity model, which was also semantically correct.These operations to generate the executable orchestration code may bevisible or “under the covers.” For instance, a user may only seegeneration of the executable orchestration code after creating asemantically correct deployment topology model. Regardless ofvisibility, the deployment model tool traverses a representation (e.g.,one or more data structures) of the one or more diagrams of thedeployment topology model. The deployment model tool begins creatingdeployment activity models until a valid deployment activity model iscreated. Although the intermediate deployment activity models (assumingthe first traversal attempt of the deployment topology model isunsuccessful) are discarded in some embodiments, other embodiments storethe representations of the intermediate failed deployment activitymodels for analysis (e.g., analysis for the tool to learn, analysis todiscover values for automatically defining attributes, etc.).

FIGS. 2-5 depict a flowchart of example operations to automaticallygenerate a deployment activity model from a semantically correctdeployment topology model. FIG. 2 depicts a first portion of theflowchart of example operations to automatically generate a deploymentactivity model from a semantically correct deployment topology model. Atblock 201, a deployment activity model is created with an initial node,final node, and one or more default activities. For instance, thedeployment topology model may model deployment of an application X. Aset of default activities are defined for application X, so that alldeployment models for application X include these default activities. Atblock 203, a source node and a destination node are determined in thedeployment topology model. Deployment may be from one or multiplesmachines or sources represented by source nodes. In addition, there maybe multiple targets of a deployment. For example, files for deploymentof application X may be sourced from three servers, and may be installedon a plurality of clients distributed over a global network. At block205, attributes of the source node and destination node are determined.At block 207, a non-global constraint(s) of the source and destinationnodes is determined.

At block 209, a loop of operations begins for each constraint. Controlflows from block 209 to block 211. At block 211, one or more action forthe constraint are performed. At block 213, it is determined if theconstraint is satisfied. If the constraint is not satisfied, thencontrol flows to block 215. If the constraint is satisfied, then controlflows to block 221.

At block 215, it is determined if an action has been defined for failureof the constraint. If an action is defined for failure, then controlflows to block 217. If an action has not been defined, then controlflows to block 219.

At block 217, the action(s) defined for the failure is performed.Control flows from block 217 back to block 211 to attempt satisfying theconstraint again.

At block 219, a notification is generated that the current deploymentactivity model does not satisfy the constraint. The notification informsa user as to the constraint that has failed to allow the user to remedythe failure. For instance, a prompt is displayed for a user to define anattribute that is needed to satisfy a constraint.

At block 221, the deployment activity model is modified in accordancewith the one or more performed actions (e.g., an activity is added tothe deployment activity model, a relationship is added, etc.). Controlflows from block 221 to block 223. The loop of operations terminates atblock 223. If each constraint has been evaluated, then control flows toblock 301 of FIG. 3. If there are additional constraints, then controlflows back to block 209.

FIG. 3 depicts a second portion of the flowchart of example operationsto automatically generate a deployment activity model from asemantically correct deployment topology model. At block 301, a path inthe deployment topology model is determined from the source node to anunmarked destination node. At block 303, a connection in the path fromthe source node is selected. The source node and activity nodes may havemultiple connections. At block 305, one or more constraints aredetermined for the selected connection. Of course, constraints may alsohave been determined for the source node. At block 307, a loop ofoperations begins for each constraint. Control flows from block 307 toblock 309.

At block 309, one or more actions for the connection constraint areperformed. At block 311, it is determined if the constraint issatisfied. If the constraint is not satisfied, then control flows toblock 315. If the constraint is satisfied, then control flows to block313.

At block 315, it is determined if an action has been defined for failureof the connection constraint. If an action is defined for failure, thencontrol flows to block 317. If an action has not been defined, thencontrol flows to block 319.

At block 317, the action(s) defined for the failure is performed.Control flows from block 317 back to block 309 to attempt satisfying theconnection constraint again.

At block 319, a notification is generated that the current deploymentactivity model does not satisfy the constraint.

At block 313, the deployment activity model is modified in accordancewith the one or more performed actions of the constraint. Control flowsfrom block 313 to block 320, which terminates the loop if allconstraints have been evaluated. Otherwise, control flows back to block307.

After all constraints have been evaluated, control flows to block 321.At block 321, it is determined if the selected connection connects tothe unmarked destination node. If the connection connects to thedestination node, then control flows to block 325. If the connectiondoes not connect to the destination node, then control flows to block323.

At block 325, the destination node is marked. At block 327, it isdetermined if all destination nodes in the deployment topology modelhave been reached. If all destination nodes have not been reached, thencontrol flows to block 301. If all destination nodes have been reached,then control flows to block 501 of FIG. 1.

If the connection did not connect to the destination node, then aconstraint(s) of the next node in the path is determined at block 323.Control flows from block 323 to block 401 of FIG. 4.

FIG. 4 depicts a third portion of the flowchart of example operations toautomatically generate a deployment activity model from a semanticallycorrect deployment topology model. At block 401, a loop of operationsbegins for each determined constraint of the next node. At block 403,one or more actions of the constraint are performed. At block 405, it isdetermined if the constraint is satisfied. If the constraint issatisfied, then control flows to block 407. If the constraint is notsatisfied, then control flows to block 411.

At block 411, it is determined if an action has been defined for failureof the node constraint. If an action is defined for failure, thencontrol flows to block 413. If an action has not been defined, thencontrol flows to block 415.

At block 413, the action(s) defined for the failure is performed.Control flows from block 413 back to block 403 to attempt satisfying thenode constraint again.

At block 415, a notification is generated that the current deploymentactivity model does not satisfy the node constraint.

At block 407, the deployment activity model is modified in accordancewith the one or more performed actions of the node constraint. Controlflows from block 407 to block 409, which terminates the loop if allconstraints of the node have been evaluated. Otherwise, control flowsback to block 401.

If all constraints of the node have been evaluated, then the nextconnection is selected at block 417. Control flows from block 417 backto block 307 of FIG. 3.

FIG. 5 depicts a fourth portion of the flowchart of example operationsto automatically generate a deployment activity model from asemantically correct deployment topology model. At block 501, it isdetermined if there are any global constraints. If there are no globalconstraints, then control flows to block 503. If there are globalconstraints of the deployment topology model, then control flows toblock 505.

At block 505, a loop of operations begins for each global constraint. Atblock 507, one or more actions of the global constraint are performed.At block 508, it is determined if the global constraint is satisfied. Ifthe global constraint is satisfied, then control flows to block 515. Ifthe global constraint is not satisfied, then control flows to block 509.

At block 509, it is determined if an action has been defined for failureof the global constraint. If an action is defined for failure, thencontrol flows to block 511. If an action has not been defined, thencontrol flows to block 513.

At block 511, the action(s) defined for the failure is performed.Control flows from block 511 back to block 507 to attempt satisfying theglobal constraint again.

At block 513, a notification is generated that the current deploymentactivity model does not satisfy the global constraint.

At block 515, the deployment activity model is modified in accordancewith the one or more performed actions of the global constraint. Controlflows from block 515 to block 517, which terminates the loop if all ofthe global constraints have been evaluated. Otherwise, control flowsback to block 505.

If all of the global constraints have been evaluated and satisfied, thencontrol flows from block 517 to block 503.

At block 503, the deployment activity model is stored.

With a semantically correct deployment activity model, executable codecan be created to implement the modeled deployment. The semanticallycorrect deployment activity model can be processed to generate one ormore executable files with executable code units that correspond to theactivities of the deployment activity model.

FIGS. 6-8 depict flowcharts of examples operations for generatingexecutable code from a semantically correct deployment activity model.FIGS. 6-7 depict a flowchart of example operations for generating alinear representation of a semantically correct deployment activitymodel. FIG. 6 depicts a first portion of the flowchart of exampleoperations for generating a linear representation of a semanticallycorrect deployment activity model. At block 601, an activity queue iscreated with an entry for the initial node of a semantically correctdeployment activity model. It should be understood that this exampleutilizes a queue to aid in understanding the inventive subject matter. Avariety of structures, however, can be used as a linear representationof the deployment activity model. Regardless the particular realization,the linear representation expresses the deployment activity model in anessentially linear manner. At block 603, a constraint(s) of an edge fromthe initial node is evaluated. At block 605, it is determined if theconstraint(s) is satisfied. If the constraint(s) is satisfied, thencontrol flows to block 611. If the constraint(s) is not satisfied, thencontrol flows to block 607.

At block 607, a notification is generated that the edge constraint isnot satisfied. For instance, a prompt is generated requesting input of avalue to define an attribute. A dashed line from block 607 to block 609indicates flow of control after some input. For example, input is readfrom a file or input by a user. At block 609, an attribute is definedfor the edge. Control flows from block 609 to block 603.

At block 611, the edge is marked and traversed. At block 613, it isdetermined if the edge terminates at the final node or an activity. Ifthe edge terminates at the final node, then control flows to block 627.If the edge terminates at an activity that is not the final node, thencontrol flows to block 615.

At block 615, the one or more constraints of the activity are evaluated.At block 617, it is determined if the evaluated constraint(s) issatisfied. If the constraint(s) is not satisfied, then control flows toblock 619. If the constraint is satisfied, then control flows to block623.

At block 619, a notification is generated that the constraint(s) was notsatisfied. After receiving input to resolve the failure, control flowsto block 622. At block 622, an attribute is defined for the activity.Control flows from block 622 back to block 615.

At block 623, the activity is queued in an activity queue. At block 625,the activity is marked. Control flows from block 625 to block 701 ofFIG. 7.

If it was determined at block 613 that the traversed edge terminated atthe final node, then it is determined if there are any unmarkedactivities at block 627. If there are unmarked activities of thedeployment activity model, then control flows to block 629. If there arenot unmarked activities of the deployment activity model, then controlflows to block 631.

At block 629, an error notification is generated. The error notificationmay identify those of the activities that were not visited duringprocessing of the deployment activity model.

At block 631, a final activity, if any, that corresponds to the finalnode is queued. At block 633, one or more executable orchestration filesare generated with the activity queue.

FIG. 7 depicts a second portion of the flowchart of example operationsfor generating an intermediate representation of a semantically correctdeployment activity model. At block 701, it is determined if there aremultiple unmarked edges from the activity. If there are multipleunmarked edges from the activity, then control flows to block 703. Ifthere is only one unmarked edge from the activity, then control flows toblock 705.

At block 703, the activity is pushed onto a traversal stack. Controlflows from block 703 to block 705.

At block 705, a constraint(s) of the unmarked edge is evaluated. Atblock 707, it is determined if the constraint(s) of the edge issatisfied. If the constraint(s) of the edge is satisfied, then controlflows to block 611 of FIG. 6. If the edge of the constraint(s) is notsatisfied, then control flows to block 709.

At block 709, it is determined if the traversal stack is empty. If thetraversal stack is empty, then control flows to block 607 of FIG. 6. Ifthe traversal stack is not empty, then control flows to block 711.

At block 711, an activity is popped from the traversal stack. Controlflows from block 711 back to block 701. When an activity has multipleedges outgoing from the activity, the edges may require traversal in aparticular order after certain conditions have been satisfied and/orafter attributes have been defined.

FIG. 8 depicts a flowchart of example operations for generatingexecutable code to orchestrate performance of the activities of thedeployment activity model. At block 801, an initial activity is dequeuedfrom the activity queue. At block 803, an executable activityorchestration file is created with a call to a code unit associated withthe initial activity. For example, a deployment model tool searches adatabase for the activity or an entry for the activity, which indicatesthe code unit (e.g., function, library file, procedure, subroutine,script, etc.). The call to the code unit is created with attributevalues for parameters. At block 805, the next activity is dequeued fromthe activity queue. At block 807, the code unit associated with thedequeued activity is determined. At block 809, a call to the determinedcode unit is created with attribute values of the activity asparameters. At block 811, it is determined if the activity queue isempty. If the activity queue is not empty, then control flows back toblock 805. If the activity queue is empty, then control flows to block813.

At block 813, the executable orchestration file is stored.

It should be understood that the above flowcharts are for understandingthe inventive subject matter, and not for limiting embodiments of theinventive subject matter. Additional operations may be performed toprocess nested elements (e.g., nested activities or nested nodes). Adeployment model may be graphically depicted with multiple deploymentdiagrams, including nested diagrams. For instance, an activity mayrepresent another activity diagram. In addition, a deployment activitymodel may include decision nodes. Additional operations would beperformed to traverse the model with decision nodes and create orlook-up code to implement the decision nodes.

To create a deployment model, or any other model, profiles are used.Typically, profiles are created manually with stereotypes. Thestereotypes define tagged values and constraints to be applied to amodel associated with the profile. The resources expended creating theseprofiles can be recovered by creating a database with metadata to beused for automated profile generation. With the structure of themetadata as stored in a database, metadata in the database can be readand transformed into one or more profiles. Automated profile generationcreates profiles efficiently and allows for flexible creation ofprofiles. For instance, the same database can be used for differentprofiles by manipulating the transformation.

FIG. 9 depicts a conceptual example of transforming metadata in adatabase into a profile. A database 905 includes metadata. In a stage A,a profile generation unit 903 reads a profile mapping scheme 901. Theprofile mapping scheme maps the structure of the database to profilestructure. Although only one mapping structure is depicted, the profilegeneration unit 903 can read multiple profiles to generate multipleprofiles or to create a single profile from multiple mapping schemes,multiple databases, etc.

The profile generation unit 903 is depicted as a sole unit in FIG. 9.The profile generation unit may interface with a deployment model tool,be a component of the deployment model tool, etc.

In a stage B, the profile generation unit reads metadata from thedatabase 905 in accordance with the profile mapping scheme 901. The oneor more entries read by the profile generation unit 901 may be selectedautomatically (e.g., as defined in the profile mapping scheme, inaccordance with a file, etc.) or manually (e.g., in accordance withparameters entered by a user).

In a stage C, the profile generation unit 903 creates a profile 907 withmetadata. The profile 907 is created in accordance with the profilemapping scheme 901. For example, the database 905 is hierarchicallystructured so that each root entry corresponds to a profile. The nextlevel corresponds to stereotypes. Although the mapping scheme may behighly detailed, the mapping scheme may also disregard details that arecan be discerned by the tool or application that reads a resultingprofile. For instance, the database may be structured to have differentlevels for constraints and tagged values. On the other hand, thedatabase may be structured so that constraints and tagged values occupythe same level. For example, the profile generation unit examinesmetadata at a same level for a distinguishing indictor (e.g., taggedvalues and constraints can be determined based on position, such asdepth or distance from a root entry, tagged values and constraints canbe determined based on a value encoded in the metadata, etc.). Theprofile generation unit 903 does not need to be aware of which metadatacorrespond to constraints and which correspond to tagged values.

In addition to automated generation of profiles, leveraging profiles todefine stereotypes for other profiles introduces efficiency intomodeling. Although constraints can be inherited, constraints cannot beaggregated across profiles. Aggregating constraints across profilesleverages the labor invested in defining other profiles as well asadding flexibility to defining stereotypes.

FIG. 10 depicts an example conceptual diagram of stereotype aggregationacross profiles. A profile A 1001 is comprised of a stereotype A and astereotype B. Stereotype A is defined with a constraint(s) A and taggedvalues. Stereotype B is defined with a constraint(s) B. A profile B 1003is comprised stereotype C and a stereotype D. Stereotype C is definedwith a constraint(s) C and tagged value(s). The stereotype D is definedwith a constraint(s) D. A profile C 1005 is defined with a stereotype E.Stereotype E is annotated with a special tag that identifies profiles Aand B 1001, 1003. Other techniques besides tagging can be used toassociated a stereotype with profiles. The stereotype definition canreference the profiles with pointers, have identifiers for the profiles,etc. The stereotype E is defined with all of the constraints of theprofiles A and B 1001, 1003. In other words, stereotype E is definedwith the aggregation of the constraints A, B, C, D.

FIG. 11 depicts a flowchart of example operations for aggregatingconstraints across profiles to define a stereotype. At block 1101, anassociation of one or more profiles is detected for a stereotype. Forexample, when a model is loaded into a system, a tool or engineprocesses the model and encounters profiles with stereotypes indicatingprofiles for constraint aggregation. At block 1103, an associatedprofile is accessed. At block 1105, one or more stereotypes of theassociated profile are copied as stereotype(s) of the subject profile.Of course, there may be zero stereotypes and one or more stereotypesassociated with other profiles. Nested profile associations or nestedconstraint aggregation can be processed recursively, although notdepicted in FIG. 11 in order not to obfuscate the inventive subjectmatter. At block 1109, it is determined if there are additional profilesassociated with the subject profile. If there are additional associatedprofiles, then control flows to block 1113. If there are no furtherassociated profiles, then control flows to block 1115.

At block 1113, the next profile associated with the subject profile isaccessed. Control flows from block 1113 back to block 1105.

At block 1115, the subject profile is committed. Committing indicatesthat the profile is available for use. Committing may involve writing toa particular memory location, setting a flag, etc.

The example operations of FIG. 11 build an in-memory representation of aprofile with a stereotype defined with aggregated constraints. AlthoughFIG. 11 assumes a pass by value type of in-memory stereotype, thestereotype may be defined in memory with a reference to the profiles.Each time the stereotype is processed, the one or more references toprofiles are followed to read the constraints of the associatedprofiles. An associated profile may include a stereotype that isassociated with another profile, thus having nested constraintaggregation.

It should be realized that the above flowcharts are of exampleoperations. The depicted operations may be performed in a differentorder, in parallel, etc. In addition, certain operations may not beperformed, and additional operations may be performed.

The described embodiments may be provided as a computer program product,or software, that may include a machine-readable medium having storedthereon instructions, which may be used to program a computer system (orother electronic device(s)) to perform a process according toembodiments of the invention(s), whether presently described or not,since every conceivable variation is not enumerated herein. A machinereadable medium includes any mechanism for storing or transmittinginformation in a form (e.g., software, processing application) readableby a machine (e.g., a computer). Examples of a machine-readable storagemedium include, but are not limited to, a magnetic storage medium (e.g.,floppy diskette); an optical storage medium (e.g., CD-ROM); amagneto-optical storage medium; read only memory (ROM); random accessmemory (RAM); erasable programmable memory (e.g., EPROM and EEPROM);flash memory; or other types of medium suitable for storing electronicinstructions. In addition, embodiments may be embodied inmachine-readable signal medium, examples of which include an electrical,optical, acoustical or other form of propagated signal (e.g., carrierwaves, infrared signals, digital signals, etc.), or wireline, wireless,or other communications medium.

FIG. 12 depicts an example computer system. A computer system includes aprocessor unit 1201 (possibly including multiple processors, multiplecores, multiple nodes, and/or implementing multi-threading, etc.). Thecomputer system includes memory 1207. The memory 1207 may be systemmemory (e.g., one or more of cache, SRAM, DRAM, zero capacitor RAM, TwinTransistor RAM, eDRAM, EDO RAM, DDR RAM, EEPROM, NRAM, RRAM, SONOS,PRAM, etc.) or any one or more of the above already described possiblerealizations of machine-readable media. The computer system alsoincludes a bus 1203 (e.g., PCI, ISA, PCI-Express, HyperTransport®,InfiniBand®, NuBus, etc.), a network interface 1209 (e.g., an ATMinterface, an Ethernet interface, a Frame Relay interface, SONETinterface, wireless interface, etc.), and a storage device(s) 1211(e.g., optical storage, magnetic storage, etc.). The computer systemalso includes a deployment modeling tool unit 1221. The deploymentmodeling tool unit 1221 embodies functionality to implement embodimentsdescribed above. The deployment modeling tool unit 1221 may be partiallyor wholly embodied as instructions encoded in the system memory 1207and/or the storage device 1211. The deployment modeling tool unit 1221may also be implemented as logic in the processor unit 1201 and/or aco-processor unit, an application specific integrated circuit, etc.Further, realizations may include fewer or additional components notillustrated in FIG. 12 (e.g., video cards, audio cards, additionalnetwork interfaces, peripheral devices, etc.). The processor unit 1201,the storage device(s) 1211, the deployment modeling tool unit 1221, andthe network interface 1209 are coupled to the bus 1203. Althoughillustrated as being coupled to the bus 1203, the memory 1207 may becoupled to the processor unit 1201.

While the embodiments are described with reference to variousimplementations and exploitations, it will be understood that theseembodiments are illustrative and that the scope of the invention(s) isnot limited to them. In general, embodiments as described herein may beimplemented with facilities consistent with any hardware system orhardware systems. Many variations, modifications, additions, andimprovements are possible.

Plural instances may be provided for components, operations orstructures described herein as a single instance. Finally, boundariesbetween various components, operations and data stores are somewhatarbitrary, and particular operations are illustrated in the context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within the scope of the invention(s). Ingeneral, structures and functionality presented as separate componentsin the exemplary configurations may be implemented as a combinedstructure or component. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents. These and other variations, modifications, additions, andimprovements may fall within the scope of the invention(s).

1. A method comprising: reading a mapping scheme that maps structure ofa database to structure of a profile, wherein the profile is for use increating a model in accordance with a modeling language; readingmetadata out from a database in accordance with the mapping scheme; andcreating an instance of the profile with the metadata read out of thedatabase in accordance with the mapping scheme, wherein said creatingthe instance of the profile with the metadata read out of the databasein accordance with the mapping scheme comprises: creating a stereotypewith metadata read from a first level in the database; reading metadatafrom a second level in the database; and determining whether themetadata read from the second level represents a constraint or a taggedvalue; wherein said determining is based on position of the metadataread form the second level with respect to the metadata read from thefirst level.
 2. The method of claim 1, wherein said determining whetherthe metadata read from the second level represents a constraint ortagged value is also based on an indicator encoded in the metadata readfrom the second level.
 3. One or more machine-readable storage mediahaving stored therein a program product, which when executed by a set ofone or more processor units causes the set of one or more processorsunits to perform operations that comprise: reading a mapping scheme thatmaps structure of a database to structure of a profile, wherein theprofile is for use in creating a model in accordance with a modelinglanguage; reading metadata out from a database in accordance with themapping scheme; and creating an instance of the profile with themetadata read out of the database in accordance with the mapping scheme,wherein said operation of creating the instance of the profile with themetadata read out of the database in accordance with the mapping schemecomprises: creating a stereotype with metadata read from a first levelin the database; reading metadata from a second level in the database;and determining whether the metadata read from the second levelrepresents a constraint or a tagged value; wherein said determiningoperation is based on position of the metadata read form the secondlevel with respect to the metadata read from the first level.
 4. Themachine-readable media of claim 3, wherein said operation of determiningwhether the metadata read from the second level represents a constraintor tagged value is also based on an indicator encoded in the metadataread from the second level.
 5. An apparatus comprising: a set of one ormore processing units; and a profile generation unit configured to, reada mapping scheme that maps structure of a database to structure of aprofile, wherein the profile is used in creating a model in accordancewith a modeling language; read metadata out from a database inaccordance with the mapping scheme; and create an instance of theprofile with the metadata read out of the database in accordance withthe mapping scheme, wherein the profile generation unit being configuredto create the instance of the profile with the metadata read out of thedatabase in accordance with the mapping scheme comprises: the profilegeneration unit being configured to create a stereotype with metadataread from a first level in the database; read metadata from a secondlevel in the database; and determine whether the metadata read from thesecond level represents a constraint or a tagged value; wherein whetherthe metadata read from the second level represents a constraint or atagged value is based on position of the metadata read from the secondlevel with respect to metadata read from the first level.